Information providing method and information providing device

ABSTRACT

In a car navigation system ( 1 ), a position information detection means ( 11 ) detects position information on a vehicle using, for example, a GPS. A travel information history of the vehicle obtained based on the detected position information is accumulated in a travel information history means ( 15 ). When detecting an event such as start of an engine, an action prediction means ( 17 ) predicts a destination of the vehicle by referring to a route to the current time and to the accumulated travel information history. Commercial or traffic information regarding the predicted destination is acquired by an information acquisition means ( 18 ) from a server ( 2 ), and then is displayed on a screen, for example, by an information provision means ( 19 ).

TECHNICAL FIELD

[0001] The present invention relates to a technology for providing userswith information by using information equipment capable of detectingposition information such as car navigation systems and personal digitalassistants (PDAs).

BACKGROUND ART

[0002] In recent years, it has become possible to detect positioninformation with car navigation systems, PDAs or cellular phones byutilizing, for example, antennas for global positioning systems (GPSs)or cellular phone networks. Accordingly, various services have beenprovided by utilizing this position detection function.

[0003] The car navigation systems offer a service of displayinginformation on the neighborhood of a user's vehicle captured with a GPSthrough FM multiplex telecasting or light beacons. With respect to thecellular phones, offered are services such as “i-area” provided by NTTDoCoMo, Inc. or “J-SKY” provided by J-PHONE Co., Ltd. Each of theservices pinpoints the area where a user of a cellular phone is locatedwithin the range from several hundreds meters to several kilometers andprovides the user with event information or service informationregarding this area.

[0004] In addition, terminals which are cellular phones including GPSunits have been put on the market, so that the position can be detectedwith an error of about several meters in an area where the precision ishigh. Various services regarding such position information are expectedto become available in the future.

[0005] Problems to be Solved

[0006] Various services now available identify the current position of(an information terminal used by) a user and provide the user withinformation regarding the current position. It is expected in the futurethat there arises the need for provision of information regarding aplace for which the user is heading as well as information regarding thecurrent position of the user.

[0007] In systems such as currently-used car navigation systems, a usercan specify a destination or a route clearly, so that a mechanism forproviding the user with information regarding the specified destinationor route can be easily attained. However, in fact, the operation ofsearching for a destination or a route is complicated. In addition, itis considered that if such operation performed by the driver during thedrive will cause momentary lapses of attention and thus obstruct carefuldriving.

[0008] In view of this, achievement of such a technology that enablesprovision of information regarding a destination of a user accuratelywithout special operation by the user will allow the user to drive morecomfortably and safely than now.

[0009] Therefore, an object of the present invention is to enableaccurate provision of information regarding the destination of the userwithout requiring the user to perform complicated operation.

DISCLOSURE OF INVENTION

[0010] In order to solve the problems, an inventive method for providinginformation to an occupant of a vehicle includes the steps of: detectingposition information on the vehicle with information equipment installedin the vehicle; accumulating, as a travel information history, routes ofthe vehicle obtained from the detected position information; predictinga destination of the vehicle by referring to a route along which thevehicle has traveled to the current time and to the accumulated travelinformation history, when detecting the occurrence of a given event; andproviding information regarding the predicted destination to theoccupant via the information equipment.

[0011] According to the present invention, when the occurrence of agiven event is detected, a destination of a vehicle is predicted byreferring to a route along which the vehicle has traveled to the currenttime and a travel information history as accumulated past travel routes.Then, information on the predicted destination is provided to anoccupant of the vehicle via information equipment installed in thevehicle. Accordingly, the vehicle occupant can receive usefulinformation regarding his/her destination appropriately without specialoperation and also can travel by the vehicle comfortably and safely.

[0012] In the inventive information providing method, the given event ispreferably a given action of the occupant.

[0013] In the inventive information providing method, the travelinformation history is preferably accumulated in the manner oftransition among nodes. In addition, at least one of the nodes ispreferably a landmark, an area or an intersection. Alternatively, theinventive method preferably includes the step of defining, as a node, anintersection which is located on the routes and through which thevehicle has passed in at least two directions. Alternatively, theinventive method preferably includes the step of defining an areaincluding a plurality of nodes satisfying a given requirement.

[0014] In the inventive information providing method, in the predictingstep, a destination to which estimated required travel time exceeds apredetermined value is preferably eliminated from being a predicteddestination.

[0015] The inventive information providing method preferably includesthe step of identifying the occupant of the vehicle, the travelinformation history is preferably accumulated for every occupant, andthe predicting step is preferably performed by referring to the travelinformation history accumulated for the occupant identified at theoccurrence of the given event.

[0016] The inventive information providing method preferably includesthe step of accumulating preference information regarding a place whichthe occupant of the vehicle prefers and the frequency with which theoccupant visited the place, and the predicting step and the informationproviding step are preferably performed in consideration of theaccumulated preference information. In addition, a place where thevehicle made a stop for at least a predetermined time period ispreferably determined to be the place which the occupant of the vehicleprefers.

[0017] The inventive information providing method preferably includesthe step of accessing a scheduler to acquire a schedule of the occupant,and the predicting step is preferably performed in consideration of theacquired schedule.

[0018] The inventive information providing method preferably includesthe steps of: accessing a scheduler to acquire a schedule of theoccupant; and providing, when the predicted destination of the vehicledeviates from that in the acquired schedule, the occupant with a messageindicating the deviation.

[0019] The inventive information providing method preferably includesthe step of calculating estimated required time or estimated arrivaltime with respect to the predicted destination, and in the informationproviding step, the estimated required time of estimated arrival timethat has been calculated is preferably provided to the occupant.

[0020] In this case, the inventive information providing methodpreferably includes the step of acquiring traffic information regardinga route to the predicted destination, and the estimated required time orestimated arrival time is preferably calculated by referring to theacquired traffic information.

[0021] Alternatively, the inventive information providing methodpreferably includes the steps of: accessing a scheduler to acquire aschedule of the occupant; and comparing the estimated required time orestimated arrival time that has been calculated with the schedule of theoccupant, thereby detecting at least one of the presence or absence ofidle time and the presence or absence of a possibility of being late. Insuch a case, when the presence of idle time is detected, information forsuggesting how to spend the idle time is preferably provided to theoccupant, in the information providing step. Otherwise, when thepresence of a possibility of being late is detected, information onanother route for shortening required time is preferably provided to theoccupant, in the information providing step.

[0022] Alternatively, the inventive information providing methodpreferably further includes the steps of: acquiring commercialinformation regarding the predicted destination; and filtering theacquired commercial information by referring to the estimated requiredtime or estimated arrival time that has been calculated, and in theinformation providing step, the filtered commercial information ispreferably provided to the occupant.

[0023] Another inventive method for providing information to an occupantof a vehicle includes the steps of: detecting position information onthe vehicle with information equipment installed in the vehicle;predicting a destination of the vehicle by referring to a route which isobtained from the detected position information and along which thevehicle has traveled to the current time and to a travel informationhistory in which routes along which the vehicle traveled in the past areaccumulated, when detecting the occurrence of a given event; andproviding information regarding the predicted destination to theoccupant via the information equipment.

[0024] An inventive method for providing information to a persontraveling includes the steps of: detecting position information on theperson with information equipment held by the person; accumulating, as atravel information history, routes of the person obtained from thedetected position information; predicting a destination of the person byreferring to a route along which the person has traveled to the currenttime and to the accumulated travel information history, when detectingthe occurrence of a given event; and providing information regarding thepredicted destination to the person via the information equipment.

[0025] An inventive system installed in a vehicle and used for providinginformation to an occupant of the vehicle includes: means for detectingposition information on the vehicle; means for accumulating, as a travelinformation history, routes of the vehicle obtained from the detectedposition information; means for predicting a destination of the vehicleby referring to a route along which the vehicle has traveled to thecurrent time and to the accumulated travel information history, when theoccurrence of a given event is detected; and means for providinginformation regarding the predicted destination to the occupant.

[0026] An inventive system held by a person and used for providinginformation to the person includes: means for detecting positioninformation on the person; means for accumulating, as a travelinformation history, routes of the person obtained from the detectedposition information; means for predicting a destination of the personby referring to a route along which the person has traveled to thecurrent time and to the accumulated travel information history, when theoccurrence of a given event is detected; and means for providinginformation regarding the predicted destination to the person.

BRIEF DESCRIPTION OF DRAWINGS

[0027]FIG. 1 is a diagram showing a configuration of the whole of asystem according to a first embodiment of the present invention.

[0028]FIG. 2 shows an example of data stored in a map database.

[0029]FIG. 3 is a flowchart showing a process of accumulating a historyof travel information in the first embodiment of the present invention.

[0030]FIG. 4 shows examples of the accumulated history of travelinformation in the first embodiment of the present invention.

[0031]FIG. 5 is a flowchart showing a process of action prediction andinformation provision in the first embodiment of the present invention.

[0032]FIG. 6 is a diagram showing an example of node transition.

[0033]FIG. 7 shows an example of position-related information stored.

[0034]FIG. 8 shows relationship between position information and URLaddresses for providing information related to the position information.

[0035]FIG. 9 shows diagrams respectively showing how to determine nodes.

[0036]FIG. 10 is a diagram showing a configuration of the whole of asystem according to a second embodiment of the present invention.

[0037]FIG. 11 shows an example of accumulated history of travelinformation in the second embodiment of the present invention.

[0038]FIG. 12 is a diagram showing a configuration of the whole of asystem according to a third embodiment of the present invention.

[0039]FIG. 13 is an example of preference information in the thirdembodiment of the present invention.

[0040]FIG. 14 is a diagram for use in describing action prediction inthe third embodiment of the present invention.

[0041]FIG. 15 is a diagram showing a configuration of the whole of asystem according to a fourth embodiment of the present invention.

[0042]FIG. 16 is an example of a schedule of a user controlled by ascheduler.

[0043]FIG. 17 is a diagram for use in describing action prediction inthe fourth embodiment of the present invention.

[0044]FIG. 18 shows an example of a message displayed when actionprediction obtained from a history of travel information differs fromthat in a schedule.

[0045]FIG. 19 is a diagram showing a configuration of the whole of asystem according to a fifth embodiment of the present invention.

[0046]FIG. 20 shows an example of the accumulated history of travelinformation in the fifth embodiment of the present invention.

[0047]FIG. 21 is an example of a travel pattern detected from the travelinformation history shown in FIG. 20.

[0048]FIG. 22 is a flowchart showing the flow of a process of actionprediction and information provision in the fifth embodiment of thepresent invention.

[0049]FIG. 23 shows an example of information for notifying a user ofestimated arrival time.

[0050]FIG. 24 is a diagram showing a configuration of the whole of asystem according to a modified example of the fifth embodiment of thepresent invention.

[0051]FIG. 25 shows an example of information to be provided to a userin the presence of idle time.

[0052]FIG. 26 shows an example of information to be provided to a userin the presence of possibility of being late.

[0053]FIG. 27 is a diagram showing a configuration of the whole of asystem according to a sixth embodiment of the present invention.

[0054]FIG. 28 shows examples of commercial information acquired.

[0055]FIG. 29 is a diagram showing a configuration of the whole of asystem according to a seventh embodiment of the present invention.

[0056]FIG. 30 is a diagram for describing definition of an areaincluding nodes.

[0057]FIG. 31 shows an example of information indicating possible areas.

BEST MODE FOR CARRYING OUT THE INVENTION

[0058] Hereinafter, embodiments of the present invention will bedescribed with reference to the drawings.

[0059] It should be noted that in the following embodiments,descriptions will be given on the assumption of a case where a userreceives information while driving a vehicle equipped with a carnavigation system. However, the present invention is not limited to thecase of car navigation systems and can be achieved by utilizinginformation equipment such as PDAs or cellular phones capable ofacquiring position information.

[0060] In the following embodiments, the ideas of “landmark” and “area”will be used. The “landmark” indicates facilities, stores or buildingssuch as stations, department stores, tourist destinations, entertainmentplaces, houses or offices. The “area” indicates an area including aplurality of landmarks (e.g., “Kobe Shigaichi” and “Kansai GakkenToshi”).

EMBODIMENT 1

[0061] A first embodiment of the present invention relates to a systemfor predicting a destination of a user based on a stored action patternof the user and providing the user with information regarding thepredicted destination.

[0062]FIG. 1 is a diagram showing a configuration of the whole of asystem according to this embodiment. In FIG. 1, reference numeral 1denotes a car navigation system installed in a vehicle in which the userrides in, reference numeral 2 denotes a server for providing informationin response to a request from the car navigation system 1, and referencenumeral 3 denotes a network, e.g., the internet, for connecting the carnavigation system 1 and the server 2. In this case, the user as avehicle occupant may be or may not be a driver.

[0063] In the car navigation system 1, reference numeral 11 denotes aposition information detection means for detecting information regardingthe current position of the user's vehicle by using a GPS, for example,and reference numeral 12 denotes a map database for storing mapinformation. Reference numeral 13 denotes a position storagedetermination means for determining whether or not the detected currentposition is to be stored (whether or not the detected current positionis a node, which will be described later) by referring to the currentposition detected by the position information detection means 11 and tothe map database 12, reference numeral 14 denotes a date and timedetection means for detecting the current date and time, and referencenumeral 15 denotes a travel information history accumulation means forchronologically storing, in pairs, the current position and the currentdate and time detected by the date and time detection means 14 when theposition storage determination means 13 determines that the currentposition should be stored. Reference numeral 16 denotes a travel patterndetection means for detecting a chronological travel pattern based oninformation on the position and the date and time (travel information)stored in the travel information history accumulation means 15, andreference numeral 17 denotes an action prediction means for predicting adestination of the user's vehicle based on the travel pattern detectedby the travel pattern detection means 16. Reference numeral 18 denotesan information acquisition means for acquiring information regarding thedestination of the user predicted by the action prediction means 17 fromthe server 2 via the network 3, and reference numeral 19 denotes aninformation provision means for displaying, to the user, the informationacquired by the information acquisition means 18 and the map informationstored in the map database 12 on a liquid crystal display, for example.

[0064] With respect to the server 2, reference numeral 21 denotes aninformation transmission/reception means for transmitting/receivinginformation to/from the car navigation system 1, and reference numeral22 denotes an information accumulation means for storing information tobe transmitted to the car navigation system 1.

[0065]FIG. 2 is a table showing an example of data stored in the mapdatabase 12 and partly excerpted from the data to be used as a referencein this embodiment. In this embodiment, intersections, landmarks and thenames of areas, for example, are indicated by the idea of “node”. InFIG. 2, node numbers have ID numbers allocated to the respective nodesand are stored together with information on the types of nodes andinformation on latitude and longitude. The latitude and longitudeinformation is position information on representative points of therespective nodes, and there exists information representing the range(e.g., a radius from each of the representative points) in accordancewith the types of the nodes, i.e., intersections, landmarks and areas.For example, in the case of intersections or landmarks, the range is setat a radius of 10 m of a representative point. In the case of areas, therange is set at a radius of 1 km of a representative point. Of course,the range may differ depending on areas.

[0066] In addition to proper names such as “ABC intersection” or “DEFamusement park”, places specific to the user such as “user's house” and“office” represented by the nodes numbers N123 and N124 can beregistered as nodes.

[0067] Instead of using ID numbers, the names of intersections,landmarks and areas may be identified by the respective nodes. That isto say, it is sufficient to identify intersections, landmarks, areas,and so on, on the route stored in the travel information historyaccumulation means 15.

[0068] In this embodiment, information on the nodes is controlled in themap database 12 provided in the car navigation system 1. Alternatively,information on the node ID numbers, for example, may be stored in theserver 2 so that when receiving position information such as latitudeand longitude from the car navigation system 1, the server 2 notifiesthe car navigation system 1 of the associated node ID number.

[0069] Now, the flow of a process in which routes of a vehicle areaccumulated in the travel information history accumulation means 15 as atravel information history will be described with reference to aflowchart shown in FIG. 3.

[0070] First, in the car navigation system 1, the position informationdetection means 11 determines whether or not the engine of the vehicleis running (S11). When the engine is running (No at S11) the positioninformation detection means 11 determines whether or not it is timing ofdetecting the current position of the vehicle (S12). This determinationis performed by determining whether or not a predetermined time period,e.g., three seconds, has elapsed after the previous detection.

[0071] When it is determined to be the timing of detection (Yes at S12),the position information detection means 11 detects the positioninformation (latitude and longitude) on the user's vehicle by using, forexample, a GPS (S13). Then, the position storage determination means 13refers to data stored in the map database 12 as shown in FIG. 2 based onthe detected position information and determines whether or not thecurrent position is contained in one of the nodes (S14). When it isdetermined that the current position is contained in one of the nodes(Yes at S14), the date and time detection means 14 detects the currentdate and time (S15). On the other hand, when it is determined that thecurrent position is not contained in any of the nodes (No at S14), theprocess returns to S11 and the determination is performed again.

[0072] When the current date and time is detected at step S15, thenumber of a node corresponding to the place where the vehicle iscurrently located, and the current date and time are stored in thetravel information history accumulation means 15 (S16). Thereafter, theprocess returns to step S11, and it is determined whether or not theengine is running again.

[0073]FIG. 4 shows examples of a travel information history accumulatedin the travel information history accumulation means 15. As shown inFIG. 4, the node numbers and the passage date and time arechronologically stored in pairs. For example, it is represented that thevehicle passed through a node N6 at 8:05 on July 31, a node N8 at 8:06on the same day, and then a node N12 at 8:08 on the same day.

[0074] In an example shown in FIG. 4(a), travel information is dividedinto segments each from a departure place to a destination, i.e., issegmented into parts each from start of the engine (i.e., departureplace) to stop of the engine (i.e., destination). On the other hand, inan example shown in FIG. 4(b), segments each “from departure from theuser's house to return to the user's house” are accumulated.Alternatively, segments each having “the same date” may be accumulatedor travel information may be accumulated without being segmented.

[0075] In this embodiment, the time is represented by month, day, hourand minute. Alternatively, units such as year, second and day of theweek may also be stored, or one of these units may be selected andstored.

[0076] In addition, the time at which the engine is started or the timeat which the engine is stopped may also be stored. Further, in additionto storage of the start time and stop time of the engine, only the nodenumber of a node through which the vehicle passed may be stored withoutstoring the passage time of the node.

[0077] Now, it will be described how the travel pattern detection means16 operates. The travel pattern detection means 16 extracts a tendencyin traveling of the user's vehicle from a travel information history asshown in FIG. 4 accumulated in the travel information historyaccumulation means 15. In this case, the tendency in traveling refers toa rule such as “if the vehicle travels along the route including thenodes N6, N8, N12 and N9 between 8 a.m. and 11 a.m., the vehicle islikely to head for a node N34 next”.

[0078] Now, everyday action of a person will be considered. The patternof the everyday action of a person does not change so much depending onthe day. For example, there seem to be tendencies as follows. Those whouse cars for commuting, they probably travel from home to the office inthe morning and travel from the office to home in the evening onweekdays. In addition, if the driver is a father, he is likely to headfor the office but if the driver is a mother, she is likely to go to asupermarket. That is to say, if position information on the place wherethe user is currently located and travel information until the currenttime are grasped, it is considered to be possible to predict the futureaction of the user to some extent.

[0079] The tendency in traveling can be extracted in the followingmanner. The entire travel information history accumulated in the travelinformation history accumulation means 15 is searched so as to find datarepresenting the route including the nodes N6, N8, N12 and N9 alongwhich the vehicle traveled between 8 a.m. and 11 a.m., and then nodenumbers after traveling along the route are extracted together with thefrequencies thereof. For example, if the vehicle travels to the node N34seventeen times, a node N18 twice and a node N17 once, after passingthrough the route including the nodes N6, N8, N12 and N9, theprobabilities of traveling to the nodes N34, N18 and N17 are representedby the following posterior probabilities:

P(N 34|R)=17/20

P(N 18|R)=2/20

P(N 17|R)=1/20

[0080] where the event in which the vehicle traveled along the routeincluding the nodes N6, N8, N12 and N9 is R. As the value of theprobability for a node increases, the probability that the user'svehicle will head for the node after the event R increases.

[0081] In this embodiment, the node N9 corresponding to the currentposition and the previous three nodes N6, N8 and N12 are used forreference. However, reference nodes are not limited to this number andmay vary in number. That is to say, the number of the nodes may beappropriately set in accordance with the accumulated data structure. Forexample, if the travel information is made by accumulating segments each“from a departure place to a destination”, a series of nodes from thenode at the departure to the node at the current time are used forreference. Alternatively, the node representing the place at which theuser made a stop just before the current time such as a landmark or anarea and the current node may be referred to. Moreover, all the nodesrepresenting landmarks or areas at which the use made a stop afterdeparture from the user's house and the current node may be referred to.

[0082] In addition, the following technique may be used. In general, asthe number of reference nodes increases, the accuracy in predictionincreases because requirements become more rigid, whereas the amount ofdata which can be extracted from the accumulated travel informationhistory decreases. As a result, from a statistical viewpoint, there is apossibility that data sufficient for the prediction cannot be obtained.In view of this, the number of reference nodes is increased by usingprevious nodes starting from the current node in reverse chronologicalorder, and a series of nodes with the maximum length satisfying therequirement that the amount of data exceeds a predetermined value isdetermined as the reference nodes. The previous nodes to be used mayinclude nodes of all the types, or may be limited to nodes of a typerepresenting any one of landmarks, areas and intersections or a typerepresenting a combination thereof By using such a technique, it ispossible to make the requirements rigid with the maximum number ofreference nodes, while satisfying the amount of data necessary forprediction in terms of statistics. As a result, the accuracy inprediction can be enhanced.

[0083] Now, action prediction on the user's vehicle and a process forproviding the user with information according to the action predictionwill be described with reference to a flowchart shown in FIG. 5.

[0084] When the occurrence of a given event is detected (S21), theaction prediction means 17 predicts future actions of the user's vehicleafter the current time by referring to the travel pattern detectionmeans 16 (S22). The operation of the travel pattern detection means 16described above may be performed beforehand or may be performed when thegiven event is detected.

[0085] Examples of the given event include start of an engine andoperation of a car navigation system (e.g., the operation of requestinginformation provision) as given actions of an occupant of the vehicle.The examples also include passing through an intersection (or after thepassage), departure and arrival from/at a landmark or an area, regulartiming such as a predetermined time interval, and acquisition of newinformation.

[0086] Now, a method for predicting the action will be described.Suppose that the user's vehicle runs along a route including the nodesN1, N2 and N3 and is now at the node N3 in FIG. 6. If the routeincluding the nodes N1, N2 and N3 is represented by the event R, theposterior probabilities that the vehicle will head for nodes N4 and N11after passing through the node N3 are represented by P(N4|R) andP(N11|R), respectively, by referring to the travel information historyaccumulated in the travel information history accumulation means 15.Since such posterior probabilities are provided to respective nodes, theprobabilities that the vehicle will head for nodes N7 and N14 arerepresented, as products of the posterior probabilities, by thefollowing equations:

P(N 7)=P(N 4|R) * P(N 6|RΛN 4) * P(N 7|RΛN 4ΛN 6)

P(N 14)=P(N 11|R) * P(N 13|RΛN 11) * P(N 14|RΛN 11ΛN 13)

[0087] The action prediction means 17 selects a node whose probabilityvalue satisfies a given requirement as a destination to be predicted.The given requirement means that the probability value exceeds athreshold value α indicating certainty of the prediction. For example,providing that, between P(N7) and P(N14), it is P(N7) that exceeds thethreshold value α, the action prediction means 17 selects the node N7 asa direction in which the user's vehicle will run.

[0088] In this embodiment, the probabilities are calculated with respectto the nodes N7 and N14. Alternatively, the action prediction means 17may calculate the probabilities of the current node and all the nodessubsequent to the current node, for example. The action prediction means17 may also calculate the products of posterior probabilities until thenode whose product is less than or equal to the threshold value α isfound, may calculate nodes through which the user passed in the past, ormay calculate only nodes representing landmarks and areas.

[0089] In the case where there is no node whose posterior probability inthe route information to the current time exceeds the threshold value α,the probabilities of nodes may be calculated every time the vehiclepasses through the nodes after the current node, and prediction on thedirection may be started when a node whose probability exceeds thethreshold value α is found.

[0090] In addition, the car navigation system 1 may be previouslynotified of nodes corresponding to tourist facilities, entertainmentplaces, stores and the like whose information is suggested to beprovided to the user or of nodes corresponding to intersections near thenodes so that the action prediction means 17 calculates theprobabilities with respect to routes to these nodes. Of course, the carnavigation system 1 may be previously notified of position informationsuch as latitude and longitude so that mapping is performed on thelatitude and longitude and the node IDs by referring to the map database12 in the car navigation system 1.

[0091] When the action prediction means 17 predicts that the user'svehicle is heading for, for example, the node N7 with any of theabove-described methods, the information acquisition means 18 transmitsposition information on the predicted node N7 to the server 2 via thenetwork 3 (S23). FIG. 2 shows that, as position information, the node N7is at 135 degrees 22 minutes 35 seconds east longitude and 34 degree 47minutes 35 seconds north latitude. When it is confirmed that theinformation transmission/reception means 21 in the server 2 receives theposition information from the car navigation system 1 (S31, S32),information accumulated in the information accumulation means 22 issearched so as to find information regarding the received positioninformation (S33).

[0092]FIG. 7 shows an example of position-related information stored inthe information accumulation means 22. As shown in FIG. 7, the names andpositions of information providers and information that the providerswant to provide are accumulated in the information accumulation means22. Then, information provided by an information provider close to theposition indicated by the position information transmitted from the carnavigation system 1 is selected as position-related information. Thedetermination whether or not the provider is close to the positionindicated by the transmitted position information is made based onwhether or not this position is included in a circle with its center atthe position of the provider and with a given radius (e.g., 400 m).Alternatively, the determination whether or not the provider is close tothe position indicated by the transmitted position information may beperformed, based on whether or not this position is included in a rangerepresented as a rectangle in which the information provider providesinformation, by identifying information for specifying the rectangle(e.g., northeast end and southwest end) with the informationaccumulation means 22 beforehand.

[0093] In this embodiment, “Sato Shop” is selected as an informationprovider close to the position indicated by the position informationtransmitted from the car navigation system 1, and “all products are 20%off today” is found as position-related information to be provided. Theposition-related information thus found is transmitted from theinformation transmission/reception means 21 to the car navigation system1 via the network 3.

[0094] The information acquisition means 18 of the car navigation system1 waits for reception of the position-related information from theserver 2 (S24). When it is determined that the information acquisitionmeans 18 receives the position-related information (Yes at S25), theinformation provision means 19 displays the received information to theuser (S26). Examples of methods for providing information includevisually displaying the information on a screen and auditorily conveyingthe information to the user by sound.

[0095] Search for the position-related information can be performed withother methods. For example, in the case where the car navigation system1 and the server 2 share a node ID system, node IDs may be previouslyassociated with respective pieces of information shown in FIG. 7 and thecar navigation system 1 may transmit the ID of a predicted node asposition information so that information on an information providerassociated with the transmitted node ID is selected as position-relatedinformation.

[0096] Alternatively, the car navigation system 1 may transmit not onlyposition information but also route information (e.g., “N3→N4→N6→N7”) soas to perform a search using a criterion such as whether or not theposition information or node ID of the information provider exists onthe extension of the route indicated by the route information as well aswhether or not the node IDs coincide with each other.

[0097] As described above, in this embodiment, it is possible to predicta destination of a user's vehicle. Accordingly, the user can receiveuseful information regarding the destination before reaching thedestination, without performing any special operation. Even if the userdoes not intend to drive toward the destination, the informationprovision can motivate the user to travel to the destination.

[0098] In this embodiment, the route of the user's vehicle isrepresented in the manner of transition among nodes correspondingintersections, landmarks, areas and the like. Accordingly, thedestination of the user can be newly predicted every time the userpasses through a node after departure. As a result, it is possible topredict a destination as quickly as possible in the middle of the route.

[0099] In this case, position information on the destination of the useris transmitted from the car navigation system 1 to the server 2.Alternatively, a table showing a relationship between positioninformation and the names or IDs of nodes may be provided in the carnavigation system 1 so that the names or IDs of nodes obtained from thepredicted position information by referring to the table is transmittedto the server 2 so as to acquire latest information.

[0100] Alternatively, a table showing a relationship between positioninformation and URL addresses of web sites which provides informationrelated to the position information as shown in FIG. 8 may be stored inthe car navigation system 1 or the server 2 so that when positioninformation is determined, information is acquired from a web sitehaving a URL address corresponding to the position information byreferring to the table.

[0101] In this embodiment, position information regarding nodes isstored together with date and time information. Alternatively, theposition information detection means 11 may detect position informationregularly to store the position information together with date and timeinformation, irrespective of whether or not the detected informationcorresponds to a node, or may not store date and time information.

[0102] In addition, in predicting a future destination, estimated traveltime required to reach the destination may be taken into consideration.This is because even if a node through which the user is quite likely topass is predicted, it is not always appropriate to provide informationregarding the node in the case where the arrival at the node is sixhours later or on the next day. That is to say, destinations to whichestimated required travel time exceeds a given time period arepreferably eliminated from being predicted destinations.

[0103] For example, suppose that the action prediction means 17 predictstwo nodes, i.e., nodes N100 and N200, as destinations of the user. Inthis case, detected position information and the date and time of thedetection are stored in pairs in the travel information historyaccumulated in the travel information history accumulation means 15.Accordingly, the travel time required to reach each of the nodes N100and N200 from the current place can be predicted from the accumulatedtravel information history.

[0104] Suppose that the idea of a threshold value to the required timeis introduced and a rule such as “nodes to which the required timeexceeds three hours are eliminated from being predicted nodes” isapplied. In addition, suppose that the required times to reach the nodesN100 and N200 are predicted to be two hours and six hours, respectively.Then, the node N200 to which the required time exceeds three hours iseliminated from being a predicted node. As a result, the actionprediction means 17 selects only the node N100 as a destination of theuser.

[0105] In this embodiment, commercial information is provided asinformation regarding predicted destinations. However, information to beprovided is not limited to the commercial information and may beinformation such as traffic information indicating a traffic jam orinformation on traffic control by the police.

[0106] In this embodiment, a travel information history is accumulatedin predicting a destination. However, the present invention is notlimited to this specific embodiment. For example, after a certain amountof the travel information history has been accumulated, the destinationmay be predicted by referring to the accumulated travel informationhistory, without further accumulation of the history.

[0107] (Method for Deciding Node)

[0108] In this embodiment, it is sufficient to decide a node byreferring to the map data beforehand. Basically, places where the user'svehicle stops (e.g., a landmark or an area) and intersections aredefined as nodes by referring to drive routes of the user's vehicle. Inaddition, nodes can be added or removed by using information on thedrive routes of the user's vehicle.

[0109] For example, as shown in FIG. 9(a), some intersections throughwhich the user's vehicle has run in two or more directions may bedefined as nodes. Specifically, intersections a and c through which theuser's vehicle has run in two directions are defined as nodes Na and Nc,whereas an intersection b through which the vehicle has run only in adirection is not defined as a node.

[0110] Thereafter, as shown in FIG. 9(b), when the user's vehicle runsthrough the intersection b in a different direction, the intersection bis added as a node Nb because the vehicle have run through theintersection b in two directions. Alternatively, as shown in FIG. 9(c),if the user's vehicle has run through the intersection a only in adirection in a past given time period, the node Na is removed. Suchsettings of nodes do not always require the map data but may beperformed using only a drive history of the user's vehicle.

EMBODIMENT 2

[0111]FIG. 10 is a diagram showing a configuration of the whole of asystem according to a second embodiment of the present invention. Inthis embodiment, even an identical car navigation system predicts actionin different manners depending on different drivers and then providesinformation. The system shown in FIG. 10 is different from that shown inFIG. 1 in that a car navigation system 1A is provided with a driveridentification means 25.

[0112] The driver identification means 25 identifies a driver when theengine of a vehicle is started, for example. Examples of techniques forthe identification include a technique of using keys or semiconductorcards individually provided to drivers and a technique of recognizing atelephone number by communicating with a cellular phone held by thedriver. These techniques are not specifically limited. A result ofidentification by the driver identification means 25 is stored in thetravel information history accumulation means 15 together with a nodenumber obtained as, for example, a user ID in the same manner as in thefirst embodiment and the time at which the user passed through the node,as shown in FIG. 11.

[0113] In this manner, a travel information history for every user isstored, thus making it possible to calculate transition probabilitiesamong nodes for every user. Accordingly, it is possible to providepredictions for respective drivers such as “the user with ID “2” drivestoward the node N12 after traveling along a route including the nodes N6and N8 in the morning” whereas “the user with ID “4” drives toward thenode N28 after traveling along a route including the nodes N6 and N8” inthe morning. With respect to such predictions, every user can beprovided with suitable and detailed information.

[0114] In addition, occupants of a vehicle other than drivers may beidentified. Specifically, if information for identifying not drivers butpassengers is stored in the travel information history accumulationmeans 15, it is possible to perform predictions such as “if the driverdrives alone, he/she will go to the node N5, whereas if the driverdrives with his/her family, they will go to the node N16”. Further,information on the driver and the information on the passenger may bestored together.

[0115] Examples of information which is preferably stored for everydriver include information on the map database 12, for example, as wellas travel information. In the first embodiment, users can register nodesto be stored in the map database 12. However, if users register a namesuch as “office” or “theater”, the object indicated by the name mightdiffer among the users. For example, the node of “office” indicates “ABCFabrication Co. Ltd. when the driver is a father, while indicating “DEFTrading Co. Ltd.” if the driver is an eldest son.

[0116] In view of this, the map database 12 may store nodes other thanproper names for every driver and may change node information to bereferred to, depending on an identification result obtained by thedriver identification means 25. In addition, the node information forevery driver may be stored separately from the map database 12. Forexample, a memory card on which the node information for every driver isrecorded, for example, may be inserted in the car navigation system 1Aso that reference is made to both of the map database 12 and the memorycard. Alternatively, the node information may be present on the network.

EMBODIMENT 3

[0117]FIG. 12 is a diagram showing a configuration of the whole of asystem according to a third embodiment of the present invention. In thisembodiment, preference information regarding places which a user prefersis previously stored in a car navigation system to provide the user withinformation to which the preference information is added together with apredicted destination. In this case, places such as a place which theuser has set in the car navigation system as a destination and a placewhere the vehicle stopped for a predetermined time period or longer withthe engine thereof halted are defined as places which the user prefers.

[0118] The system shown in FIG. 12 is different from that shown in FIG.1 in that a car navigation system 1B is provided with a stop positiondetection means 31, a set destination analysis means 32 and a preferenceinformation accumulation means 33. The stop position detection means 31determines whether or not the vehicle has stopped for a predeterminedtime period or longer with the engine thereof halted and detectslatitude and longitude information as position information on the user'svehicle that has stopped. The set destination analysis means 32 analyzeswhere a destination set in the car navigation system 1B by the user islocated. The preference information accumulation means 33 refers to themap database 12 and stores information representing the frequency aboutthe place detected by the stop position detection means 31 and the placeanalyzed by the set destination analysis means 32.

[0119]FIG. 13 shows an example of data stored in the preferenceinformation accumulation means 33. As shown in FIG. 13, each data itemhas attributes of position, name and frequency. In this case, when theuser sets a destination, the map database 12 is searched with theposition on the map, the telephone number, the name and like attributesused as keys. Therefore, almost all the places to be analyzed by the setdestination analysis means 32 have names. On the other hand, positioninformation detected by the stop position detection means 31 does notnecessarily have a name registered for the search in the map database12. Therefore, there exists data whose name has not been set yet such asin the lowermost line in the FIG. 13. Of course, if there is a nodecorresponding to position information, it is sufficient to describe theID or name of the node.

[0120] The preference information is updated in the following manner.That is to say, when a place detected by the stop position detectionmeans 31 or the set destination analysis means 32 has been alreadyregistered in data, the frequency about the place is increased by one,whereas when the place is unregistered, new data is added and thefrequency is set at one.

[0121] It will be described how an action prediction means 17A of thisembodiment operates with reference to FIG. 14.

[0122] Suppose that the user's vehicle is now at a node N103 by way ofnodes N101 and N102. Then, a probability P(N106) that the user's vehiclewill run toward a node N106 by way of nodes N104 and N105 and aprobability P(112) that the vehicle will run toward a node N112 by wayof a node N111 can be obtained by the method described in the firstembodiment. Suppose that

P(N 106)=0.6, P(N 112)=0.4

[0123] where a threshold value α=0.5. Then, it is determined that thevehicle is likely to run toward the node N106 in the first embodiment.

[0124] In the third embodiment, the preference information accumulationmeans 33 is additionally used for reference. From the data shown in FIG.13, it is found that the user fairly frequently visits Family Plazalocated in the direction to the node N112. The proportion P(famipla) offrequency with which the vehicle stops at Family Plaza among all theplaces where the vehicle has ever stopped is 0.64(=64/(25+64+6+5). Then,a formula for calculating a value in consideration of both actionprediction and preference information is defined as follows:

{P(N 112)+P(famipla)}/2

[0125] Then, the calculation with this formula obtains a value of 0.52that exceeds the threshold value α. Accordingly, the action predictionmeans 17A outputs two possible directions toward the respective nodesN106 and N112 as a prediction on future action of the user.

[0126] The information acquisition means 18 receives the node numbersN106 and N112 from the action prediction means 17A and transmits thereceived node numbers to the server 2 via the network 3. The subsequentoperation of information provision is the same as in the firstembodiment. The action prediction means 17A may output latitude andlongitude information on the nodes N106 and N112 or the names offacilities and stores such as Sato Shop or Family Plaza, instead of thenode numbers.

[0127] As described above, according to this embodiment, information isprovided in consideration of preference information on the user as wellas action prediction on the user. Accordingly, the user can acquireinformation regarding places which the user prefers other than a placefor which the user is heading. As a result, the user have a wide rangeof choice in future action such as changing the schedule based on theacquired information, thus making it possible to propose new action tothe user.

[0128] In this embodiment, the proportion of frequency regarding a placewhich the user prefers is obtained in consideration of frequenciesregarding all the places accumulated in the preference informationaccumulation means 33. Instead, the proportion may be obtained inconsideration of the frequency of only preferred places to which theuser is expected to drive.

[0129] In this embodiment, action prediction is performed by the methoddescribed in the first embodiment. However, a function of consideringpreference information can be added as in this embodiment and iseffective with any other method for action prediction.

EMBODIMENT 4

[0130]FIG. 15 is a diagram showing a configuration of the whole of asystem according to a fourth embodiment of the present invention. Inthis embodiment, a schedule controlling function is utilized in additionto the action prediction so as to improve the accuracy in actionprediction. The system shown in FIG. 15 is different from that shown inFIG. 1 in that an action prediction means 17B in a car navigation system1C refers to a scheduler 41 provided with a function of controlling aschedule such as a PDA in predicting the action of a user. Examples oftechniques for allowing the car navigation system 1C to refer to thescheduler 41 include a technique of communicating with equipment havinga scheduling function such as a PDA or a cellular phone with or withouta cable, a technique of using a memory card such as an SD card as amedium, and a technique of referring to schedule information present onthe network. However, the present invention is not limited to thesetechniques.

[0131]FIG. 16 shows an example of schedule information controlled by thescheduler 41. The schedule in FIG. 16 shows that the user has a meetingin a home office from 10:00, moves to a research laboratory between12:00 and 14:00, observes the laboratory from 14:00, and then goes homeat 18:00.

[0132] A procedure of predicting an action by referring to the scheduler41 will be described with reference to FIG. 17.

[0133] Suppose that the user leaves the home office around a node N201and is now at a node N203 by way of a node N202 and the current time is13:00. Then, the action prediction means 17 of the first embodimentcalculates, referring to a travel information history, a probabilityP(N206) that the user will head for the node N206 at which the researchlaboratory is located and a probability P(N212) that the user will headfor the node N212 at which a factory is located, shown by the followingequations:

P(N 206)=0.4, P(N 212)=0.4

[0134] A threshold value indicating the certainty of prediction is 0.7.Any of the probabilities P(N206) and P(N212) does not exceed thethreshold value and the direction in which the user is heading cannot bedetermined.

[0135] On the other hand, the action prediction means 17B of thisembodiment acquires a schedule of the user from the scheduler 41 inaddition to the travel information history, and refers to the acquiredschedule and travel information history. It is described in thescheduler 41 that the user observes the research laboratory from 14:00.When it is determined that the research laboratory is located at thenode N206 from the map database 12, the probability P(N206|schedule)that the user will head for the node N206 before 14:00 obtained from theschedule is defined, for example, as follows:

P(N 206|schedule)=0.5

[0136] Then, the action prediction means 17B adds the predicted valueP(N206)(=0.4) obtained from the travel information history to thepredicted value P(N206|schedule) (=0.5), thereby obtaining 0.9 as afinal predicted value. Since this obtained value exceeds the thresholdvalue, i.e., 0.7, it is found that the user is likely to head for thenode N206 as a future action. As a result, the action prediction means17B predicts the direction toward the node N206 as a direction in whichthe user will travel and the information acquisition means 18 acquiresinformation regarding the direction toward the node N206, thereby makingit possible to provide the information to the user.

[0137] In this manner, according to this embodiment, the schedule of theuser is grasped by using information equipment having a schedulingfunction such as a PDA in combination so that a destination is predictedin consideration of the schedule, thus allowing a prediction with higheraccuracy. For example, in the case where it is difficult to determinethe direction of traveling by using only the predicted value obtainedfrom the travel information history, the direction may be predicted byadding the predicted value obtained from the schedule with reference tothe schedule of the user.

[0138] The predicted value obtained from the schedule is fixed at agiven value (0.5). Alternatively, this predicted value may be defined invarious manners, e.g., may be obtained based on a past record concerningwhether or not the user has acted as scheduled in the past, or may beset differently depending on places such that a value with respect to aplace where the user acted as scheduled in the past is set relativelyhigh.

[0139] If information to be acquired from the scheduler 41 includes notonly names such as “research laboratory” but also information regardingthe position thereof (e.g., latitude and longitude information or a nodeID), it is possible to predict a place whose name is not included in themap database 12.

[0140] In addition, in the case where the destination obtained from thetravel information history deviates from that in the schedule acquiredfrom the scheduler 41, a process for displaying a massage showing thedeviation may be performed. FIG. 18 shows an example of a displayingscreen of a car navigation system when such a massage is displayed. Asshown in FIG. 18, if the action prediction and the schedule deviatesfrom each other, a massage indicating that the destination for which theuser is heading is different from that in the schedule is displayed tothe user. In this case, the address and telephone number of the placefor which the user is to be heading according to the schedule may beincluded in the massage. Alternatively, if the user's vehicle isequipped with a telephone function, a telephone call may beautomatically made according to the request of the user. Further, aplace where the user is to visit according to the schedule may be newlyset as a destination in the car navigation system so as to notify theuser of the route to the destination.

[0141] In this embodiment, the action prediction is performed by themethod described in the first embodiment. However, the referencefunction of the scheduler can be added as in this embodiment and iseffective with any other method.

EMBODIMENT 5

[0142]FIG. 19 is a diagram showing a configuration of the whole of asystem according to a fifth embodiment of the present invention. In thisembodiment, a destination of the user is predicted, and in addition,information such as estimated required time or estimated arrival timeregarding the destination is presented. The system shown in FIG. 19 isdifferent from that shown in FIG. 1 in that a required time calculationmeans 51 for calculating estimated time required to reach a predicteddestination based on information acquired by the information acquisitionmeans 18 via the network 13.

[0143]FIG. 20 shows an example of a travel information historyaccumulated in the travel information history accumulation means 15 inthis embodiment. In the example shown in FIG. 20, dates, times ofdeparture, places of departure, routes and destinations are accumulatedas travel information. The places of departure are places where theengine was started, and the destinations are places where the engine washalted.

[0144] Examples of techniques for expressing places of departure anddestinations include various techniques. For example, if the place wherethe engine was halted is a landmark or an area stored in the mapdatabase 12, it is possible to express the place by the name or the nodeID. If there is neither a landmark nor an area corresponding to theplace where the engine was halted, the place may be expressed by a nameor a node ID of a landmark or area at a small distance (within apredetermined distance) from the place or of a representative (famous)landmark or area located near the place. Alternatively, the place may berepresented by the name or node ID of a nearby intersection or theaddress of the neighborhood of the place.

[0145] With respect to a place where the engine is frequently halted, amessage urging the user to register the name of the place is output atgiven timing. Further, the name corresponding to the place (e.g., “Ms.A's house”) may be automatically registered by referring to an addressbook, for example, held by the user or existing on the network.

[0146] A travel pattern detection means 16 refers to a travelinformation history as shown in FIG. 20 to detect a travel pattern asshown in FIG. 21. Specifically, the past drive of the user as shown inFIG. 20 is classified according to groups such as day of the week ofdeparture or time of departure. In each of the groups, a set ofdeparture places, routes and destinations are expressed using theaverage required time and the frequency. For example, FIG. 21 shows thatin the morning on weekdays, if the departure place is the user's house,the destinations of ABC Co. Ltd., PQR Clinic and XYZ Supermarket havefrequencies of 70%, 25% and 5%, respectively.

[0147] Now, action prediction according to this embodiment and the flowof a process for providing information to the user according to theaction prediction will be described with reference to a flowchart shownin FIG. 22.

[0148] When detecting, for example, the start of the engine as a givenevent (S41), the action prediction means 17 acquires current date andtime and information on the current position (S42 and S43). Theinformation on the current position can be acquired by utilizing theposition information detection means 11. Alternatively, a place wherethe engine was halted in the previous drive may be obtained by referringto the travel information history accumulation means 15 and defined asthe current position. In this manner, information on the currentposition can be obtained quickly.

[0149] Suppose that the current date and time is 9:10 on a weekday andthe current position is the user's house. Then, the destination of theuser is predicted by referring to information as shown in FIG. 21 anddetected by the travel pattern detection means 16. (S44). In FIG. 21, inthe case where the user left the user's house as a place of departurebetween 8:00 and 11:00 on a weekday, the destinations are ABC Co. Ltd.,PQR Clinic and XYZ Supermarket at frequencies of 70%, 25% and 5%,respectively. If the idea of a threshold value for showing the certaintyof prediction as described in the first embodiment is introduced anddestinations with frequencies of 20% or higher are defined as possibledestinations, ABC Co. Ltd. and PQR Clinic are predicted. When thedestination is predicted by the action prediction means 17, theinformation acquisition means 18 transmits, to the server 2, informationon routes to the respective predicted destinations (S45).

[0150] Upon receiving the route information from a car navigation system1D (S51), the server 2 searches traffic information accumulated in theinformation accumulation means 22 to find information concerning theroute received (S52) and transmits the traffic information found to thecar navigation system 1D (S53).

[0151] In the car navigation system 1D, when the information acquisitionmeans 18 receives the traffic information (S46), the required timecalculation means 51 calculates estimated travel time required to reacheach of the possible destinations by referring to the received trafficinformation and the time periods required to reach the destinationsshown in FIG. 20 (S47). For example, estimated required time iscalculated with current traffic information taken into consideration asfollows: “it takes 60 minutes from the user's house to ABC Co. Ltd.normally but 80 minutes in consideration of traffic information” or “ittakes 20 minutes from the user's house to PQR Clinic without any trafficjam”. Since the current time is 9:10, it is calculated that theestimated arrival time at ABC Co. Ltd. is 10:30 and the estimatedarrival time at PQR Clinic is 9:30.

[0152] When the required time calculation means 51 calculates theestimated times of arrival to the respective possible destinations, theinformation provision means 19 provides the user with the calculatedinformation (S48). FIG. 23 shows an example of information presented ona screen of the car navigation system 1D. In FIG. 23, in addition to theestimated times of arrival, related traffic information is provided tothe user. Instead of the estimated times of arrival, informationregarding estimated required time may be provided.

[0153] In this embodiment, the estimated required time to reach each ofthe predicted destinations is calculated based on the average requiredtime in the past drive of the user and the current traffic information.Alternatively, the distance from the current position to the destinationmay be measured by referring to the map database 12 to roughly estimatethe required time utilizing average speed of the vehicle.

Modified Example

[0154] In addition, in the case where comparison is made betweenestimated required time or estimated arrival time and the schedule ofthe user to determine the presence or absence of idle time or thepresence or absence of possibility of being late, to find the presenceof idle time or the possibility of being late, it is possible to providenew information.

[0155]FIG. 24 is a diagram showing a configuration of the whole of asystem according to a modified example of this embodiment. As comparedto the system shown in FIG. 19, a determination means 52 for comparing acalculation result obtained by the required time calculation means 51with the user's schedule obtained from the scheduler 41 is provided. Thedetermination means 52 compares the estimated required time or estimatedarrival time that has been calculated with the user's schedule, therebydetecting the presence of idle time or the possibility of being late.

[0156] For example, provided that the user is scheduled to go to aresearch laboratory at 15:00 according to the scheduler 41. If thecurrent time is 13:10 and the estimated time required to reach theresearch laboratory from the current position is 50 minutes, it is foundthat there is idle time of about one hour. If the determination means 52detects the presence of the idle time of about one hour, the informationprovision means 19 provides the user with information for suggesting howto spend the idle time. For example, as shown in FIG. 25, the user isprovided with information regarding recommended places (e.g., coffeeshops and bookstores) which is located near the route to the researchlaboratory and which the user can visit within the idle time of aboutone hour.

[0157] Provided that the user is scheduled to go to the researchlaboratory at 15:00 according to the scheduler 41, the current time is14:20 and the estimated travel time required to reach the researchlaboratory from the current position is 50 minutes, it is found thatthere is a possibility that the user will be slightly behind time. Ifthe determination means 52 detects the possibility of being late, theinformation provision means 19 provides the user with information onanother route with which the required time to reach the destination canbe shortened. For example, in an example shown in FIG. 26, the user isrecommended to use a highway running in parallel with the route to theresearch laboratory.

EMBODIMENT 6

[0158]FIG. 27 is a diagram showing a configuration of the whole of asystem according to a sixth embodiment of the present invention. Thesystem shown in FIG. 27 is different from that shown in FIG. 1 in thatthe required time calculation means 51 described in the fifth embodimentand a filtering means 61 for filtering acquired commercial informationby referring to estimated required time or estimated arrival timeobtained by the required time calculation means 51 are provided.

[0159] Suppose that the action prediction means 17 predicts the nodes Aand B as destinations of the user's vehicle. Then, the informationacquisition means 18 acquires traffic information on the routes to thenodes A and B and commercial information related to the nodes A and Bfrom the server 2 via the network 3.

[0160]FIG. 28 shows an example of commercial information acquired. Asshown in FIG. 28, souvenirs are presented during the period from 9:00 onJune 1 to 15:00 on June 2 at Children Plaza at the node A and femaledrivers receive a gas discount at GS Umeda at the node B. In this case,it is assumed that the required time calculation means 51 predicts thatthe arrival time at the node A is 15:20 on June 2 and the arrival timeat the node B is 16:00 on June 2.

[0161] The filtering means 61 determines whether or not the predictedarrival times are included in an effective period of the commercialinformation and outputs only pieces of the information included therein.In this case, the estimated arrival time at Children Plaza at the nodeA, which is 15:20 on June 2, is after the effective period. Accordingly,the information on Children Plaza is eliminated from being informationto be provided. On the other hand, the estimated arrival time at GSUmeda at the node B, which is 16:00 on June 2, is within the effectiveperiod. Accordingly, information on GS Umeda is determined to be usefulto the user and thus is output to the information provision means 19.

[0162] Alternatively, instead of being eliminated, commercialinformation for which the estimated arrival time is after the effectiveperiod may be presented with a message notifying the user of highpossibility of not being in time. Alternatively, the possibility ofbeing in time may be displayed as a numerical value to call attention ofthe user. Further, the estimated arrival time may be repeatedly updatedin accordance with a travel status of the user's vehicle and a trafficjam status so that value indicating the possibility of being in time ischanged from moment to moment in accordance with updating of theestimated arrival time.

[0163] Furthermore, the commercial information may be filtered in theserver 2.

EMBODIMENT 7

[0164]FIG. 29 is a diagram showing a configuration of the whole of asystem according to a seventh embodiment of the present invention. Thesystem shown in FIG. 29 is different from that shown in FIG. 1 in thatan area definition means 71 for defining “area” including a plurality ofnodes using a travel history information is provided.

[0165] Suppose that there are nodes 1 through 7 as shown in FIG. 30(a).In this case, the area definition means 71 refers to a travelinformation history accumulated in the travel information historyaccumulation means 15, and if there are a plurality of nodes which arelocated within a predetermined range of area and which the user hasvisited a given number of times or more, the area definition means 71defines the nodes as an “area”. In an example shown in FIG. 30(b), thenodes 2, 3, 5 and 6 are brought together within an area.

[0166] When predicting that the destination of the user is the node 3,the action prediction means 17 refers to information on the area definedby the area definition means 71 to recognize that the nodes 2, 5 and 6are included in the area also including the node 3. Then, the actionprediction means 17 instructs the information acquisition means 18 toacquire not only information regarding the node 3 but also informationregarding the nodes 2, 5 and 6. As a result, not only the informationregarding the node 3 but also information regarding the nodes 2, 5 and 6included in the same area are presented to the user.

[0167] The range of nodes to be included in an area may be changeddepending on the distance from the user's house or the office where theuser stays mainly. For example, the area may be enlarged, as thedistance from the user's house increases. The range may be also changeddepending on the transportation means of the user. For example, in thecase where the transportation by car is a premise as in the case of carnavigation systems, the range may be set wide, while in the case wherethe transportation on foot is a premise, the range may be set narrow.

[0168] The technique for defining an area is not limited to thisembodiment. For example, the following technique may be used. As shownin FIG. 31, groups of nodes which can be defined as areas are previouslydefined corresponding to respective possible areas (e.g., an area 1corresponding to “Umeda area” and an area 2 corresponding to “Nambaarea”) Then, when one of possible areas satisfies a requirement that thenumber of user's visits exceeds a predetermined number with respect to agiven number or more of nodes out of the corresponding groups of nodes,the possible area is defined as an area. Alternatively, nodes where theuser visits on the same day or in the same time of day may be defined asan area.

[0169] In addition, the area definition as described above may beperformed on areas already defined by considering the areas to be nodes.

[0170] Further, the area definition may be performed by previouslysetting a large area (e.g., “Higashi-Osaka area”) so that, when thenumber of nodes included in the area increases, the area is divided. Inthe case of dividing an area to define new areas, the methods describedabove may be used.

[0171] In the foregoing embodiments, description is given on theassumption that a car navigation system is used as equipment forproviding information to the user. However, in the present invention,information equipment is not limited to car navigation systems. Even aninformation terminal such as a cellular phone or a PDA that the userholds everyday may be used so long as sensing for position informationcan be performed. In such a case, the same advantages as that obtainedin the embodiments can be achieved.

[0172] In the foregoing embodiments, the user is assumed to travel bycar. However, even the cases where the user travels by means other thancar, e.g., on foot or by train, the present invention is applicable solong as the user holds information equipment.

[0173] In the embodiments, examples in which the travel informationhistory accumulation means 15, travel pattern detection means 16, andaction prediction means 17 are provided inside information equipmentsuch as a car navigation system. Alternatively, these means may beprovided in an external server connected by a network. Specifically, aconfiguration in which information detected by the position informationdetection means 11 or the date and time detection means 14 istransmitted to the external server and accumulated therein so that whena given event happens, the server predicts a future action to sendnecessary information to the car navigation system may be used. Such aconfiguration is effective especially when the information equipmentheld by the user is a cellular phone or a PDA.

[0174] In the embodiments, a travel pattern is extracted from anaccumulated travel information history. Alternatively, the travelpattern may be acquired from outside such as the network or may be setby the user himself/herself For example, when an information providerwants “to provide a user who is running along a route passing throughnode A→node B→node C with information on a shop C near a node T”, thetravel pattern detection means 16 is made to store this requirement.Then, when the user actually runs along the route passing through nodeA→node B→anode C, the node T is predicted as a direction in which theuse will travel, so that the information acquisition means 18 requestsinformation regarding the vicinity of the node T of the server 2.

[0175] In addition, to acquire information, a VICS or airwaves may beutilized instead of the network. In such a case, the informationacquisition means 18 may extract only information regarding thedestination predicted by the action prediction means 17 to provide theinformation to the user.

[0176] As described above, according to the present invention, adestination of the user is predicted based on the travel informationhistory of the user and the route along which the user has traveled tothe current time. Accordingly, even if the user does not perform anyspecial operation, information on facilities or shops relating to thedestination can be provided to the user appropriately.

1. An information providing method for providing information to anoccupant of a vehicle, the method comprising the steps of: detectingposition information on the vehicle with information equipment installedin the vehicle; accumulating, as a travel information history, routes ofthe vehicle obtained from the detected position information; predictinga destination of the vehicle by referring to a route along which thevehicle has traveled to the current time and to the accumulated travelinformation history, when detecting the occurrence of a given event; andproviding information regarding the predicted destination to theoccupant via the information equipment.
 2. The method of claim 1,wherein the given event is a given action of the occupant.
 3. The methodof claim 1, wherein the travel information history is accumulated in themanner of transition among nodes.
 4. The method of claim 3, wherein atleast one of the nodes is a landmark, an area or an intersection.
 5. Themethod of claim 3, including the step of defining, as a node, anintersection which is located on the routes and through which thevehicle has passed in at least two directions.
 6. The method of claim 3,including the step of defining an area including a plurality of nodessatisfying a given requirement.
 7. The method of claim 1, wherein in thepredicting step, a destination to which estimated required travel timeexceeds a predetermined value is eliminated from being a predicteddestination.
 8. The method of claim 1, including the step of identifyingthe occupant of the vehicle, wherein the travel information history isaccumulated for every occupant, and the predicting step is performed byreferring to the travel information history accumulated for the occupantidentified at the occurrence of the given event.
 9. The method of claim1, including the step of accumulating preference information regarding aplace which the occupant of the vehicle prefers and the frequency withwhich the occupant visited the place, wherein the predicting step andthe information providing step are performed in consideration of theaccumulated preference information.
 10. The method of claim 9, wherein aplace where the vehicle made a stop for at least a predetermined timeperiod is determined to be the place which the occupant of the vehicleprefers.
 11. The method of claim 1, including the step of accessing ascheduler to acquire a schedule of the occupant, wherein the predictingstep is performed in consideration of the acquired schedule.
 12. Themethod of claim 1, including the steps of: accessing a scheduler toacquire a schedule of the occupant; and providing, when the predicteddestination of the vehicle deviates from that in the acquired schedule,the occupant with a message indicating the deviation.
 13. The method ofclaim 1, including the step of calculating estimated required time orestimated arrival time with respect to the predicted destination,wherein in the information providing step, the estimated required timeor estimated arrival time that has been calculated is provided to theoccupant.
 14. The method of claim 13, including the step of acquiringtraffic information regarding a route to the predicted destination,wherein the estimated required time or estimated arrival time iscalculated by referring to the acquired traffic information.
 15. Themethod of claim 13, including the steps of accessing a scheduler toacquire a schedule of the occupant; and comparing the estimated requiredtime or estimated arrival time that has been calculated with theschedule of the occupant, thereby detecting at least one of the presenceor absence of idle time and the presence or absence of a possibility ofbeing late.
 16. The method of claim 15, wherein when the presence ofidle time is detected, information for suggesting how to spend the idletime is provided to the occupant, in the information providing step. 17.The method of claim 15, wherein when the presence of a possibility ofbeing late is detected, information on another route for shorteningrequired time is provided to the occupant, in the information providingstep.
 18. The method of claim 13, further including the steps of:acquiring commercial information regarding the predicted destination;and filtering the acquired commercial information by referring to theestimated required time or estimated arrival time that has beencalculated, wherein in the information providing step, the filteredcommercial information is provided to the occupant.
 19. An informationproviding method for providing information to an occupant of a vehicle,the method comprising the steps of: detecting position information onthe vehicle with information equipment installed in the vehicle;predicting a destination of the vehicle by referring to a route which isobtained from the detected position information and along which thevehicle has traveled to the current time and to a travel informationhistory in which routes along which the vehicle traveled in the past areaccumulated, when detecting the occurrence of a given event; andproviding information regarding the predicted destination to theoccupant via the information equipment.
 20. An information providingmethod for providing information to a person traveling, the methodcomprising the steps of: detecting position information on the personwith information equipment held by the person; accumulating, as a travelinformation history, routes of the person obtained from the detectedposition information; predicting a destination of the person byreferring to a route along which the person has traveled to the currenttime and to the accumulated travel information history, when detectingthe occurrence of a given event; and providing information regarding thepredicted destination to the person via the information equipment. 21.An information providing system installed in a vehicle and used forproviding information to an occupant of the vehicle, the systemcomprising: means for detecting position information on the vehicle;means for accumulating, as a travel information history, routes of thevehicle obtained from the detected position information; means forpredicting a destination of the vehicle by referring to a route alongwhich the vehicle has traveled to the current time and to theaccumulated travel information history, when the occurrence of a givenevent is detected; and means for providing information regarding thepredicted destination to the occupant.
 22. An information providingsystem held by a person and used for providing information to theperson, the system comprising: means for detecting position informationon the person; means for accumulating, as a travel information history,routes of the person obtained from the detected position information;means for predicting a destination of the person by referring to a routealong which the person has traveled to the current time and to theaccumulated travel information history, when the occurrence of a givenevent is detected; and means for providing information regarding thepredicted destination to the person.